Data Scientist – Predictive Modeling (Property Insurance)

_Job Summary:

_We are looking for a skilled Data Scientist focused on predictive modeling to join our Actuarial team. This key technical position, reporting directly to the Data Science Manager, encompasses the entire modeling lifecycle, including model design, data, development, validation, and implementation. In this role, you will be an integral part of a collaborative team, working with various functions within the Actuarial team and across departments to drive data-driven decision-making.

This is a remote position working under EST business hours.

Salary Range: $90,000 - $120,000

Applicants must be legally authorized to work for ANY employer in the U.S.; Kingstone does not currently sponsor individuals for employment-based visas.

What you will do:

• Design, build, and deploy pricing, underwriting, and customer demand models to continuously improve Kingstone’s profitability and growth.
• Oversee end-to-end model development and application.
• Continuously refresh and enhance existing models.
• Own data management for the modeling function, including data collection, integration, cleaning, processing and transformation.
• Explore new data sources and analytical techniques to ensure competitive advantage.
• Communicate analysis, insights, and results to team, business partners, and leaders.
• Share expertise with the business and the actuarial community through discussions, presentations, or peer reviews to improve business outcomes.

What you will bring:

• An advanced degree in Statistics, Data Science, or a related field; or a bachelor’s degree with 4+ years of predictive modeling experience.
• 2+ years of experience working in a data science modeling development position in the insurance field.
• Experience working for a personal lines P&C insurance carrier, preferably with experience in the Homeowners business.
• Expertise in Microsoft Office tools, SQL, and R or Python.
• Hands-on modeling experience with large-scale datasets and a deep understanding of predictive modeling, data mining, and other quantitative and research analytics (Generalized Linear Models, Multivariate Analysis, Bayesian Methods, Non-Linear Models, Decision Trees, Parametric estimation, Machine learning techniques, etc.).
• Intensive experience managing, processing and analyzing data for modeling purposes.
• Industry expertise in predictive analytics tools, regression models, and software (preferably Earnix) is required.
• Self-motivation and capability of independently and proactively advancing projects.
• Strong willingness to learn and attention to detail.
• Excellent communications skills including verbal, written, and presentation skills.
• Collaborative strengths and ability to work as part of a team as well as cross-functionally.
• Ability to develop and deliver communications for complex technical projects to non-technical audiences (appropriate audience, level of detail, urgency).

What we offer:

• Comprehensive health benefits
• Life and disability insurance
• 401(k) with company match
• Paid parental leave
• Paid vacation, paid holidays, and birthday off
• Flexible work schedule
• Peer recognition program

Kingstone is an Equal Opportunity Employer that does not discriminate on the basis of actual or perceived race, creed, color, religion, alienage or national origin, ancestry, citizenship status, age, disability or handicap, sex, marital status, veteran status, sexual orientation, gender identity, genetic information, arrest record, or any other characteristic protected by applicable federal, state or local laws.

Please note that Kingstone does not accept unsolicited resumes from recruiters or employment agencies. In the event of a recruiter or agency submitting a resume or candidate without a signed agreement being in place, we explicitly reserve the right to pursue and hire such candidates without any financial obligation to the recruiter or agency. Any unsolicited resumes, including those submitted directly to hiring managers, are deemed to be the property of Kingstone.